Online recovery in cluster databases
EDBT '08 Proceedings of the 11th international conference on Extending database technology: Advances in database technology
Using quantitative analysis to implement autonomic IT systems
ICSE '09 Proceedings of the 31st International Conference on Software Engineering
Resource provisioning for cloud computing
CASCON '09 Proceedings of the 2009 Conference of the Center for Advanced Studies on Collaborative Research
Predicting completion times of batch query workloads using interaction-aware models and simulation
Proceedings of the 14th International Conference on Extending Database Technology
Dolly: virtualization-driven database provisioning for the cloud
Proceedings of the 7th ACM SIGPLAN/SIGOPS international conference on Virtual execution environments
Integrated estimation and tracking of performance model parameters with autoregressive trends
Proceedings of the 2nd ACM/SPEC International Conference on Performance engineering
Database replication: a tutorial
Replication
Transactional auto scaler: elastic scaling of in-memory transactional data grids
Proceedings of the 9th international conference on Autonomic computing
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This paper introduces a transparent self-configuring architecture for automatic scaling with temperature awareness in the database tier of a dynamic content web server. We use a unified approach to achieving the joint objectives of performance, efficient resource usage and avoiding temperature hot-spots in a replicated database cluster. The key novelty in our approach is a lightweight on-line learning method for fast adaptations to bottleneck situations. Our approach is based on deriving a lightweight performance model of the replicated database cluster on the fly. The system trains its own model based on perceived correlations between various system and application metrics and the query latency for the application. The model adjusts itself dynamically to changes in the application workload mix. We use our performance model for query latency prediction and determining the number of database replicas necessary to meet the incoming load. We adapt by adding the necessary replicas, pro-actively in anticipation of a bottleneck situation and we remove them automatically in underload. Finally, the system adjusts its query scheduling algorithm dynamically in order to avoid temperature hotspots within the replicated database cluster. We investigate our transparent database provisioning mechanism in the database tier using the TPC-W industry-standard e-commerce benchmark. We demonstrate that our technique provides quality of service in terms of both performance and avoiding hot-spot machines under different load scenarios. We further show that our method is robust to dynamic changes in the workload mix of the application.